This book offers a hands-on introduction to building and understanding federated learning (FL) systems. FL enables multiple devices -- such as smartphones, sensors, or local computers -- to collaboratively train machine learning (ML) models, while keeping their data private and local.
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Adaptive client participation mechanism for federated learning in heterogeneous vehicular networks. [PDF]
Lin W, Zhou Y.
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Quantifying energy and accuracy trade-offs of federated learning on wearable health devices. [PDF]
S R, Khekare G, Kumar Y, Soni G.
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Federated Learning with Differential Privacy for Ultrasound Breast Cancer Classification: An Empirical Study. [PDF]
Makhanov N +3 more
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Editorial Note: A scalable blockchain-enabled federated learning architecture for edge computing. [PDF]
PLOS One Editors.
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Quantum federated learning for autonomous vehicle cybersecurity: An analytical review of architectures and threat landscapes. [PDF]
T S, Devadas RM.
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Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network. [PDF]
Liu ZP +8 more
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Federated Learning Based on Fuzzy Fusion Rules for Chemical Production Process Fault Diagnosis. [PDF]
Xu Y, Yang W, Du S, Zhang M.
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FedMIR: Multimodal Federated Learning with Missing Modality Imputation and Distribution-Aware Routing. [PDF]
Xiong H, Dai M.
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Adaptive Multi-Model Hierarchical Federated Learning for Robust IoT Intrusion Detection. [PDF]
Latif S, Djenouri D.
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